Repeat self-harm hospitalizations in Canada: a survival analysis
Bibliographic record
Abstract
BACKGROUND: Repeat self-harm hospitalizations are associated with a greater risk of suicide and place a substantial burden on the healthcare system. In Canada, despite growing awareness of self-harm as a public heath issue, most existing research has focused on the prevalence of self-harm, with less attention given to repeat admissions. This study aims to assess the risk of repeat self-harm hospitalizations in Canada and identify population subgroups at higher risk. METHODS: We included 74,055 patients discharged between April 2016 and March 2022, with self-harm hospitalizations recorded in the Canadian Institute for Health Information's Discharge Abstract Database and the Ontario Mental Health Reporting System. After an initial self-harm hospitalization, patients were followed for repeat admissions during the study period. The risk of readmission was estimated using Kaplan-Meier survival analysis, while hazard ratios for factors such as sex, age group, method of self-harm and the presence of a mental disorder diagnosis, were calculated using Cox regression models. RESULTS: Among patients hospitalized for self-harm, the risk of readmission was 9.3% within one year and 13.0% within three years of the index hospitalization. Three-quarters of readmissions occurred within the first year, and 90% occurred within two years. Females had a higher risk of readmission than males (hazard ratio = 1.32), with the highest risk observed among females aged 10-14 years (19.2% within three years), while patients aged 65 years and older had the lowest risk for both males and females. Females who self-harmed by cutting and patients of both sexes who used substance-related poisoning methods, as well as patients with a mental disorder diagnosis, were also at greater risk of readmissions. CONCLUSION: In Canada, approximately one in ten patients hospitalized for self-harm were readmitted, with most readmissions occurring within the subsequent first year. Certain subgroups, including females, young girls, individuals who engaged in self-harm through cutting or substance use, and those with a mental disorder, face higher risks. This study provides insights to guide targeted interventions aimed at preventing recurrence, informing resource allocation, and emphasizing the need for comprehensive mental health support to improve outcomes for at-risk individuals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".